Overview

Dataset statistics

Number of variables18
Number of observations304017
Missing cells98172
Missing cells (%)1.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory32.5 MiB
Average record size in memory112.0 B

Variable types

Numeric14
Categorical4

Alerts

df_index is highly correlated with is_ckdHigh correlation
result is highly correlated with 17341.0High correlation
is_ckd is highly correlated with df_indexHigh correlation
17341.0 is highly correlated with resultHigh correlation
age_2022 has 3464 (1.1%) missing values Missing
bmi has 31258 (10.3%) missing values Missing
16263.0 has 6345 (2.1%) missing values Missing
17339.0 has 6345 (2.1%) missing values Missing
17341.0 has 6345 (2.1%) missing values Missing
2688.0 has 6345 (2.1%) missing values Missing
3086.0 has 6345 (2.1%) missing values Missing
5254.0 has 6345 (2.1%) missing values Missing
5272.0 has 6345 (2.1%) missing values Missing
582.0 has 6345 (2.1%) missing values Missing
8574.0 has 6345 (2.1%) missing values Missing
921.0 has 6345 (2.1%) missing values Missing
bmi is highly skewed (γ1 = 108.8855736) Skewed
582.0 is highly skewed (γ1 = 78.35617597) Skewed
921.0 is highly skewed (γ1 = 130.5942952) Skewed
df_index is uniformly distributed Uniform
df_index has unique values Unique
16263.0 has 263697 (86.7%) zeros Zeros
17339.0 has 276178 (90.8%) zeros Zeros
17341.0 has 276861 (91.1%) zeros Zeros
2688.0 has 263324 (86.6%) zeros Zeros
3086.0 has 266889 (87.8%) zeros Zeros
5254.0 has 267460 (88.0%) zeros Zeros
5272.0 has 271117 (89.2%) zeros Zeros
582.0 has 264591 (87.0%) zeros Zeros
8574.0 has 260845 (85.8%) zeros Zeros
921.0 has 268086 (88.2%) zeros Zeros

Reproduction

Analysis started2022-11-27 12:03:27.966266
Analysis finished2022-11-27 12:04:49.871225
Duration1 minute and 21.9 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct304017
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean152008
Minimum0
Maximum304016
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-11-27T13:04:49.990721image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15200.8
Q176004
median152008
Q3228012
95-th percentile288815.2
Maximum304016
Range304016
Interquartile range (IQR)152008

Descriptive statistics

Standard deviation87762.29273
Coefficient of variation (CV)0.5773531178
Kurtosis-1.2
Mean152008
Median Absolute Deviation (MAD)76004
Skewness2.335736098 × 10-19
Sum4.621301614 × 1010
Variance7702220026
MonotonicityNot monotonic
2022-11-27T13:04:50.162314image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
480011
 
< 0.1%
2580631
 
< 0.1%
1661861
 
< 0.1%
2634931
 
< 0.1%
1494291
 
< 0.1%
1401401
 
< 0.1%
2305041
 
< 0.1%
1868941
 
< 0.1%
1997731
 
< 0.1%
1450811
 
< 0.1%
Other values (304007)304007
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
3040161
< 0.1%
3040151
< 0.1%
3040141
< 0.1%
3040131
< 0.1%
3040121
< 0.1%
3040111
< 0.1%
3040101
< 0.1%
3040091
< 0.1%
3040081
< 0.1%
3040071
< 0.1%

POD
Real number (ℝ≥0)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.164550009
Minimum0
Maximum5
Zeros236
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-11-27T13:04:50.317967image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5110233636
Coefficient of variation (CV)0.4388161605
Kurtosis20.04789417
Mean1.164550009
Median Absolute Deviation (MAD)0
Skewness4.031585024
Sum354043
Variance0.2611448781
MonotonicityNot monotonic
2022-11-27T13:04:50.430063image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1266297
87.6%
229158
 
9.6%
35207
 
1.7%
41786
 
0.6%
51333
 
0.4%
0236
 
0.1%
ValueCountFrequency (%)
0236
 
0.1%
1266297
87.6%
229158
 
9.6%
35207
 
1.7%
41786
 
0.6%
51333
 
0.4%
ValueCountFrequency (%)
51333
 
0.4%
41786
 
0.6%
35207
 
1.7%
229158
 
9.6%
1266297
87.6%
0236
 
0.1%

result
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size297.1 KiB
0
244257 
1
59760 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters304017
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0244257
80.3%
159760
 
19.7%

Length

2022-11-27T13:04:50.565049image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-27T13:04:50.735960image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0244257
80.3%
159760
 
19.7%

Most occurring characters

ValueCountFrequency (%)
0244257
80.3%
159760
 
19.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number304017
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0244257
80.3%
159760
 
19.7%

Most occurring scripts

ValueCountFrequency (%)
Common304017
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0244257
80.3%
159760
 
19.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII304017
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0244257
80.3%
159760
 
19.7%

age_2022
Real number (ℝ≥0)

MISSING

Distinct753
Distinct (%)0.3%
Missing3464
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean63.94571274
Minimum12.2
Maximum99.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-11-27T13:04:50.894340image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum12.2
5-th percentile36.3
Q153.3
median67.3
Q375.2
95-th percentile84.6
Maximum99.2
Range87
Interquartile range (IQR)21.9

Descriptive statistics

Standard deviation14.89617461
Coefficient of variation (CV)0.2329503257
Kurtosis-0.4936904832
Mean63.94571274
Median Absolute Deviation (MAD)9.8
Skewness-0.5166684741
Sum19219075.8
Variance221.8960179
MonotonicityNot monotonic
2022-11-27T13:04:51.235276image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69.41554
 
0.5%
741517
 
0.5%
72.61468
 
0.5%
73.61399
 
0.5%
36.81390
 
0.5%
72.21343
 
0.4%
75.61341
 
0.4%
69.51338
 
0.4%
74.81321
 
0.4%
74.11298
 
0.4%
Other values (743)286584
94.3%
(Missing)3464
 
1.1%
ValueCountFrequency (%)
12.25
< 0.1%
12.66
< 0.1%
12.78
< 0.1%
13.82
 
< 0.1%
14.11
 
< 0.1%
14.21
 
< 0.1%
14.57
< 0.1%
151
 
< 0.1%
15.14
< 0.1%
15.23
 
< 0.1%
ValueCountFrequency (%)
99.222
 
< 0.1%
97.810
 
< 0.1%
97.41
 
< 0.1%
97.317
 
< 0.1%
96.527
< 0.1%
95.71
 
< 0.1%
95.636
< 0.1%
95.364
< 0.1%
95.220
 
< 0.1%
9526
< 0.1%

sex
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size297.1 KiB
1
158937 
0
145080 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters304017
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1158937
52.3%
0145080
47.7%

Length

2022-11-27T13:04:51.391548image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-27T13:04:51.542108image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1158937
52.3%
0145080
47.7%

Most occurring characters

ValueCountFrequency (%)
1158937
52.3%
0145080
47.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number304017
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1158937
52.3%
0145080
47.7%

Most occurring scripts

ValueCountFrequency (%)
Common304017
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1158937
52.3%
0145080
47.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII304017
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1158937
52.3%
0145080
47.7%

is_dia
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size297.1 KiB
0
248536 
1
55481 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters304017
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0248536
81.8%
155481
 
18.2%

Length

2022-11-27T13:04:51.700385image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-27T13:04:51.856345image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0248536
81.8%
155481
 
18.2%

Most occurring characters

ValueCountFrequency (%)
0248536
81.8%
155481
 
18.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number304017
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0248536
81.8%
155481
 
18.2%

Most occurring scripts

ValueCountFrequency (%)
Common304017
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0248536
81.8%
155481
 
18.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII304017
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0248536
81.8%
155481
 
18.2%

is_ckd
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
289540 
1
 
14477

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters304017
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0289540
95.2%
114477
 
4.8%

Length

2022-11-27T13:04:51.997366image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-27T13:04:52.159101image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0289540
95.2%
114477
 
4.8%

Most occurring characters

ValueCountFrequency (%)
0289540
95.2%
114477
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number304017
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0289540
95.2%
114477
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
Common304017
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0289540
95.2%
114477
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII304017
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0289540
95.2%
114477
 
4.8%

bmi
Real number (ℝ≥0)

MISSING
SKEWED

Distinct713
Distinct (%)0.3%
Missing31258
Missing (%)10.3%
Infinite0
Infinite (%)0.0%
Mean64.86753269
Minimum14.5
Maximum430015.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-11-27T13:04:52.301066image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum14.5
5-th percentile20.5
Q124.8
median27.9
Q331.4
95-th percentile38.2
Maximum430015.5
Range430001
Interquartile range (IQR)6.6

Descriptive statistics

Standard deviation3948.325825
Coefficient of variation (CV)60.86751971
Kurtosis11854.20481
Mean64.86753269
Median Absolute Deviation (MAD)3.3
Skewness108.8855736
Sum17693203.35
Variance15589276.82
MonotonicityNot monotonic
2022-11-27T13:04:52.465202image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.43496
 
1.1%
30.13404
 
1.1%
28.73348
 
1.1%
262978
 
1.0%
27.52837
 
0.9%
28.12721
 
0.9%
27.82695
 
0.9%
28.32669
 
0.9%
28.42610
 
0.9%
292429
 
0.8%
Other values (703)243572
80.1%
(Missing)31258
 
10.3%
ValueCountFrequency (%)
14.520
 
< 0.1%
1510
 
< 0.1%
15.11
 
< 0.1%
15.63
 
< 0.1%
15.848
 
< 0.1%
15.937
 
< 0.1%
16.1158
0.1%
16.334
 
< 0.1%
16.458
 
< 0.1%
16.547
 
< 0.1%
ValueCountFrequency (%)
430015.523
 
< 0.1%
365.327
 
< 0.1%
265.735
 
< 0.1%
255.814
 
< 0.1%
216.32
 
< 0.1%
1903
 
< 0.1%
188.88
 
< 0.1%
163.639
 
< 0.1%
156.121
 
< 0.1%
135.7171
0.1%

16263.0
Real number (ℝ≥0)

MISSING
ZEROS

Distinct174
Distinct (%)0.1%
Missing6345
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean1.429889946
Minimum0
Maximum24.8
Zeros263697
Zeros (%)86.7%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-11-27T13:04:52.632264image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile12.6
Maximum24.8
Range24.8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.05033893
Coefficient of variation (CV)2.832622847
Kurtosis5.132096957
Mean1.429889946
Median Absolute Deviation (MAD)0
Skewness2.593238497
Sum425638.2
Variance16.40524545
MonotonicityNot monotonic
2022-11-27T13:04:52.788035image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0263697
86.7%
11.6773
 
0.3%
11.7755
 
0.2%
11.8747
 
0.2%
12.4693
 
0.2%
11.5688
 
0.2%
12.5687
 
0.2%
11.3674
 
0.2%
11.4673
 
0.2%
12.6668
 
0.2%
Other values (164)27617
 
9.1%
(Missing)6345
 
2.1%
ValueCountFrequency (%)
0263697
86.7%
6.71
 
< 0.1%
71
 
< 0.1%
7.11
 
< 0.1%
7.31
 
< 0.1%
7.43
 
< 0.1%
7.51
 
< 0.1%
7.64
 
< 0.1%
7.714
 
< 0.1%
7.86
 
< 0.1%
ValueCountFrequency (%)
24.81
< 0.1%
24.71
< 0.1%
24.51
< 0.1%
24.11
< 0.1%
242
< 0.1%
23.81
< 0.1%
23.62
< 0.1%
23.51
< 0.1%
23.41
< 0.1%
23.31
< 0.1%

17339.0
Real number (ℝ≥0)

MISSING
ZEROS

Distinct226
Distinct (%)0.1%
Missing6345
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean0.08801126408
Minimum0
Maximum2.48
Zeros276178
Zeros (%)90.8%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-11-27T13:04:52.947726image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1.02
Maximum2.48
Range2.48
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3342808023
Coefficient of variation (CV)3.798159313
Kurtosis13.7326298
Mean0.08801126408
Median Absolute Deviation (MAD)0
Skewness3.838571742
Sum26198.489
Variance0.1117436548
MonotonicityNot monotonic
2022-11-27T13:04:53.115130image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0276178
90.8%
1.46250
 
0.1%
1.49249
 
0.1%
1.51242
 
0.1%
1.48228
 
0.1%
1.5222
 
0.1%
1.56222
 
0.1%
1.43221
 
0.1%
1.3219
 
0.1%
1.37218
 
0.1%
Other values (216)19423
 
6.4%
(Missing)6345
 
2.1%
ValueCountFrequency (%)
0276178
90.8%
0.063
 
< 0.1%
0.078
 
< 0.1%
0.089
 
< 0.1%
0.097
 
< 0.1%
0.112
 
< 0.1%
0.1112
 
< 0.1%
0.1215
 
< 0.1%
0.1319
 
< 0.1%
0.1415
 
< 0.1%
ValueCountFrequency (%)
2.482
 
< 0.1%
2.42
 
< 0.1%
2.291
 
< 0.1%
2.271
 
< 0.1%
2.261
 
< 0.1%
2.253
< 0.1%
2.245
< 0.1%
2.232
 
< 0.1%
2.212
 
< 0.1%
2.27
< 0.1%

17341.0
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct273
Distinct (%)0.1%
Missing6345
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean0.08255712328
Minimum0
Maximum4
Zeros276861
Zeros (%)91.1%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-11-27T13:04:53.283215image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.97
Maximum4
Range4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3192488781
Coefficient of variation (CV)3.867005843
Kurtosis15.28244103
Mean0.08255712328
Median Absolute Deviation (MAD)0
Skewness3.972998361
Sum24574.944
Variance0.1019198462
MonotonicityNot monotonic
2022-11-27T13:04:53.435086image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0276861
91.1%
1.28262
 
0.1%
1.3260
 
0.1%
1.23244
 
0.1%
1.34234
 
0.1%
1.32231
 
0.1%
1.36230
 
0.1%
1.24229
 
0.1%
1.33229
 
0.1%
1.27226
 
0.1%
Other values (263)18666
 
6.1%
(Missing)6345
 
2.1%
ValueCountFrequency (%)
0276861
91.1%
0.064
 
< 0.1%
0.077
 
< 0.1%
0.086
 
< 0.1%
0.094
 
< 0.1%
0.14
 
< 0.1%
0.1110
 
< 0.1%
0.1214
 
< 0.1%
0.1315
 
< 0.1%
0.1421
 
< 0.1%
ValueCountFrequency (%)
41
< 0.1%
3.721
< 0.1%
3.241
< 0.1%
3.071
< 0.1%
3.031
< 0.1%
3.011
< 0.1%
2.991
< 0.1%
2.941
< 0.1%
2.871
< 0.1%
2.841
< 0.1%

2688.0
Real number (ℝ≥0)

MISSING
ZEROS

Distinct680
Distinct (%)0.2%
Missing6345
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean28.29271144
Minimum0
Maximum1334
Zeros263324
Zeros (%)86.6%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-11-27T13:04:53.606955image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile249
Maximum1334
Range1334
Interquartile range (IQR)0

Descriptive statistics

Standard deviation83.25009559
Coefficient of variation (CV)2.94245731
Kurtosis9.981910682
Mean28.29271144
Median Absolute Deviation (MAD)0
Skewness3.066350974
Sum8421948
Variance6930.578415
MonotonicityNot monotonic
2022-11-27T13:04:53.772288image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0263324
86.6%
230236
 
0.1%
225231
 
0.1%
242228
 
0.1%
217227
 
0.1%
251224
 
0.1%
243223
 
0.1%
227222
 
0.1%
229215
 
0.1%
219215
 
0.1%
Other values (670)32327
 
10.6%
(Missing)6345
 
2.1%
ValueCountFrequency (%)
0263324
86.6%
71
 
< 0.1%
161
 
< 0.1%
171
 
< 0.1%
201
 
< 0.1%
222
 
< 0.1%
231
 
< 0.1%
253
 
< 0.1%
263
 
< 0.1%
271
 
< 0.1%
ValueCountFrequency (%)
13341
< 0.1%
12761
< 0.1%
12001
< 0.1%
11381
< 0.1%
11091
< 0.1%
11081
< 0.1%
10651
< 0.1%
10641
< 0.1%
10601
< 0.1%
9851
< 0.1%

3086.0
Real number (ℝ≥0)

MISSING
ZEROS

Distinct329
Distinct (%)0.1%
Missing6345
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean0.6772323228
Minimum0
Maximum44.9
Zeros266889
Zeros (%)87.8%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-11-27T13:04:53.949809image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5.8
Maximum44.9
Range44.9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.279789349
Coefficient of variation (CV)3.366332752
Kurtosis33.79354158
Mean0.6772323228
Median Absolute Deviation (MAD)0
Skewness4.732914862
Sum201593.1
Variance5.197439477
MonotonicityNot monotonic
2022-11-27T13:04:54.101626image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0266889
87.8%
4.9736
 
0.2%
5.3732
 
0.2%
5.2717
 
0.2%
5.4716
 
0.2%
5712
 
0.2%
5.1711
 
0.2%
5.6700
 
0.2%
4.7695
 
0.2%
4.8694
 
0.2%
Other values (319)24370
 
8.0%
(Missing)6345
 
2.1%
ValueCountFrequency (%)
0266889
87.8%
0.61
 
< 0.1%
0.71
 
< 0.1%
0.81
 
< 0.1%
1.11
 
< 0.1%
1.22
 
< 0.1%
1.37
 
< 0.1%
1.49
 
< 0.1%
1.51
 
< 0.1%
1.69
 
< 0.1%
ValueCountFrequency (%)
44.91
 
< 0.1%
421
 
< 0.1%
41.31
 
< 0.1%
41.11
 
< 0.1%
40.91
 
< 0.1%
39.81
 
< 0.1%
39.52
< 0.1%
39.31
 
< 0.1%
38.93
< 0.1%
38.41
 
< 0.1%

5254.0
Real number (ℝ≥0)

MISSING
ZEROS

Distinct337
Distinct (%)0.1%
Missing6345
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean0.4457423607
Minimum0
Maximum7.15
Zeros267460
Zeros (%)88.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-11-27T13:04:54.306629image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4.38
Maximum7.15
Range7.15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.332568814
Coefficient of variation (CV)2.989549416
Kurtosis5.326957869
Mean0.4457423607
Median Absolute Deviation (MAD)0
Skewness2.686783763
Sum132685.02
Variance1.775739644
MonotonicityNot monotonic
2022-11-27T13:04:54.459199image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0267460
88.0%
4.38348
 
0.1%
4.42346
 
0.1%
4.37345
 
0.1%
4.26344
 
0.1%
4.39338
 
0.1%
4.29337
 
0.1%
4.41336
 
0.1%
4.36335
 
0.1%
4.33334
 
0.1%
Other values (327)27149
 
8.9%
(Missing)6345
 
2.1%
ValueCountFrequency (%)
0267460
88.0%
1.471
 
< 0.1%
2.251
 
< 0.1%
2.321
 
< 0.1%
2.341
 
< 0.1%
2.531
 
< 0.1%
2.591
 
< 0.1%
2.81
 
< 0.1%
2.811
 
< 0.1%
2.891
 
< 0.1%
ValueCountFrequency (%)
7.151
< 0.1%
7.131
< 0.1%
7.111
< 0.1%
6.941
< 0.1%
6.842
< 0.1%
6.731
< 0.1%
6.61
< 0.1%
6.571
< 0.1%
6.521
< 0.1%
6.491
< 0.1%

5272.0
Real number (ℝ≥0)

MISSING
ZEROS

Distinct242
Distinct (%)0.1%
Missing6345
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean12.48369212
Minimum0
Maximum156.1
Zeros271117
Zeros (%)89.2%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-11-27T13:04:54.624669image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile139.8
Maximum156.1
Range156.1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation39.89527661
Coefficient of variation (CV)3.195791457
Kurtosis6.32172952
Mean12.48369212
Median Absolute Deviation (MAD)0
Skewness2.884041815
Sum3716045.6
Variance1591.633096
MonotonicityNot monotonic
2022-11-27T13:04:54.957244image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0271117
89.2%
140.5522
 
0.2%
140.1522
 
0.2%
140.6515
 
0.2%
140.7513
 
0.2%
140508
 
0.2%
140.3506
 
0.2%
140.2503
 
0.2%
140.9498
 
0.2%
139.8493
 
0.2%
Other values (232)21975
 
7.2%
(Missing)6345
 
2.1%
ValueCountFrequency (%)
0271117
89.2%
119.81
 
< 0.1%
120.41
 
< 0.1%
121.41
 
< 0.1%
121.72
 
< 0.1%
121.91
 
< 0.1%
122.61
 
< 0.1%
122.91
 
< 0.1%
123.21
 
< 0.1%
123.32
 
< 0.1%
ValueCountFrequency (%)
156.11
< 0.1%
150.71
< 0.1%
150.11
< 0.1%
1502
< 0.1%
149.41
< 0.1%
149.31
< 0.1%
149.21
< 0.1%
149.11
< 0.1%
1491
< 0.1%
148.81
< 0.1%

582.0
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct463
Distinct (%)0.2%
Missing6345
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean0.07392075506
Minimum0
Maximum69.02
Zeros264591
Zeros (%)87.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-11-27T13:04:55.133174image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.56
Maximum69.02
Range69.02
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.435419973
Coefficient of variation (CV)5.890361545
Kurtosis9962.448743
Mean0.07392075506
Median Absolute Deviation (MAD)0
Skewness78.35617597
Sum22004.139
Variance0.1895905529
MonotonicityNot monotonic
2022-11-27T13:04:55.284243image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0264591
87.0%
0.44874
 
0.3%
0.46820
 
0.3%
0.43803
 
0.3%
0.4799
 
0.3%
0.47795
 
0.3%
0.49786
 
0.3%
0.45777
 
0.3%
0.48761
 
0.3%
0.42755
 
0.2%
Other values (453)25911
 
8.5%
(Missing)6345
 
2.1%
ValueCountFrequency (%)
0264591
87.0%
0.142
 
< 0.1%
0.152
 
< 0.1%
0.169
 
< 0.1%
0.174
 
< 0.1%
0.1811
 
< 0.1%
0.1917
 
< 0.1%
0.222
 
< 0.1%
0.2135
 
< 0.1%
0.2235
 
< 0.1%
ValueCountFrequency (%)
69.021
< 0.1%
65.891
< 0.1%
63.271
< 0.1%
62.371
< 0.1%
58.411
< 0.1%
51.591
< 0.1%
46.491
< 0.1%
45.811
< 0.1%
32.241
< 0.1%
30.281
< 0.1%

8574.0
Real number (ℝ≥0)

MISSING
ZEROS

Distinct2156
Distinct (%)0.7%
Missing6345
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean11.22580962
Minimum0
Maximum944.8
Zeros260845
Zeros (%)85.8%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-11-27T13:04:55.451319image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile86.7
Maximum944.8
Range944.8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation33.8360032
Coefficient of variation (CV)3.014125872
Kurtosis56.60047254
Mean11.22580962
Median Absolute Deviation (MAD)0
Skewness5.089295214
Sum3341609.2
Variance1144.875112
MonotonicityNot monotonic
2022-11-27T13:04:55.602043image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0260845
85.8%
79.499
 
< 0.1%
72.894
 
< 0.1%
70.894
 
< 0.1%
76.693
 
< 0.1%
82.591
 
< 0.1%
77.191
 
< 0.1%
77.490
 
< 0.1%
73.490
 
< 0.1%
73.789
 
< 0.1%
Other values (2146)35996
 
11.8%
(Missing)6345
 
2.1%
ValueCountFrequency (%)
0260845
85.8%
25.61
 
< 0.1%
26.71
 
< 0.1%
28.12
 
< 0.1%
28.51
 
< 0.1%
31.63
 
< 0.1%
31.91
 
< 0.1%
32.31
 
< 0.1%
33.31
 
< 0.1%
33.51
 
< 0.1%
ValueCountFrequency (%)
944.81
< 0.1%
914.61
< 0.1%
899.61
< 0.1%
888.21
< 0.1%
8681
< 0.1%
850.41
< 0.1%
835.11
< 0.1%
819.81
< 0.1%
818.91
< 0.1%
810.71
< 0.1%

921.0
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct384
Distinct (%)0.1%
Missing6345
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean0.05374306619
Minimum0
Maximum95.06
Zeros268086
Zeros (%)88.2%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-11-27T13:04:55.775293image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.43
Maximum95.06
Range95.06
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.439472813
Coefficient of variation (CV)8.177293261
Kurtosis23396.85194
Mean0.05374306619
Median Absolute Deviation (MAD)0
Skewness130.5942952
Sum15997.806
Variance0.1931363534
MonotonicityNot monotonic
2022-11-27T13:04:55.932791image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0268086
88.2%
0.381058
 
0.3%
0.391055
 
0.3%
0.41003
 
0.3%
0.37981
 
0.3%
0.36961
 
0.3%
0.41959
 
0.3%
0.43953
 
0.3%
0.34953
 
0.3%
0.35944
 
0.3%
Other values (374)20719
 
6.8%
(Missing)6345
 
2.1%
ValueCountFrequency (%)
0268086
88.2%
0.111
 
< 0.1%
0.132
 
< 0.1%
0.142
 
< 0.1%
0.164
 
< 0.1%
0.175
 
< 0.1%
0.1812
 
< 0.1%
0.1920
 
< 0.1%
0.233
 
< 0.1%
0.2140
 
< 0.1%
ValueCountFrequency (%)
95.061
< 0.1%
88.331
< 0.1%
85.111
< 0.1%
72.881
< 0.1%
63.871
< 0.1%
56.951
< 0.1%
43.421
< 0.1%
43.011
< 0.1%
32.521
< 0.1%
30.531
< 0.1%

Interactions

2022-11-27T13:04:44.051384image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:03:54.317805image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:03.243286image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:07.553483image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:10.636082image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:13.786542image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:16.799086image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:19.850203image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:23.280179image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:26.441160image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:29.533487image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:34.278022image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:37.778365image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:40.875112image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:44.326985image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:03:55.434280image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:03.599364image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:07.801557image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:10.877124image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:14.028388image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:17.042500image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:20.292248image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:23.528428image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:26.675060image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:29.864089image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:34.539115image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:38.018405image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:41.107228image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:44.561654image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:03:56.351466image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:03.916426image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:08.017370image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:11.084290image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:14.234034image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:17.249200image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:20.513053image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:23.744040image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:26.876051image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:30.191059image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:34.813151image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:38.238232image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:41.307230image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:44.787618image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:03:57.069439image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:04.216586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:08.223387image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:11.291037image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:14.436035image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:17.453101image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:20.730369image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:23.948055image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:27.068347image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:30.680755image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:35.049204image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:38.442622image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:41.498057image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:45.064012image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:03:57.842452image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T13:04:04.748365image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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Correlations

2022-11-27T13:04:56.089096image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-27T13:04:56.382946image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-27T13:04:56.670924image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-27T13:04:56.962240image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-27T13:04:57.155964image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-27T13:04:47.293083image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-27T13:04:47.975992image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-11-27T13:04:49.064062image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-11-27T13:04:49.492903image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexPODresultage_2022sexis_diais_ckdbmi16263.017339.017341.02688.03086.05254.05272.0582.08574.0921.0
0480011176.9100NaN0.00.000.000.00.00.00.00.0134.50.0
1152021179.3000NaN0.00.000.000.00.00.0144.50.00.00.0
21748751070.010030.100.00.000.000.00.00.00.00.095.40.0
31346031170.800030.100.00.000.000.08.20.00.00.00.00.0
41727041068.600028.700.01.370.000.00.00.00.00.00.00.0
51667651054.400026.6010.90.000.000.00.00.00.00.00.00.0
61932681167.200026.100.01.100.000.00.00.00.00.00.00.0
72827001168.110025.500.00.000.000.00.00.0134.40.00.00.0
81327191073.710025.850.00.000.00218.00.00.00.00.00.00.0
9492601189.000041.300.00.000.450.00.00.00.00.00.00.0

Last rows

df_indexPODresultage_2022sexis_diais_ckdbmi16263.017339.017341.02688.03086.05254.05272.0582.08574.0921.0
3040071575431065.610028.20.00.000.00.00.00.000.00.0073.70.00
3040082389891178.810026.20.00.000.00.00.00.000.00.00115.60.00
30400972841083.500128.612.50.000.00.00.00.000.00.000.00.00
30401025786211NaN00033.00.00.430.00.00.00.000.00.000.00.00
304011854944063.4100NaN9.70.000.00.00.00.000.00.000.00.00
3040121518261048.800018.80.00.000.00.00.00.000.00.330.00.00
3040132180151067.510046.50.00.000.00.00.04.090.00.000.00.00
3040142242721041.501021.30.01.720.00.00.00.000.00.000.00.00
3040151276511073.900025.710.40.000.00.00.00.000.00.000.00.00
3040162053181072.700026.40.00.000.00.00.00.000.00.000.00.24